{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import sys \n",
    "from os import getcwd, path\n",
    "sys.path.append(path.dirname(getcwd()))\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from utils import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from cohorts.functions import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from cohorts.variant_filters import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from utils.data import rizvi_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from cohorts.varcode_utils import FilterableVariant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def filter_30(filterable_variant):\n",
    "    somatic_stats = variant_stats_from_variant(filterable_variant.variant,\n",
    "                                               filterable_variant.variant_metadata)\n",
    "    return (\n",
    "        somatic_stats.tumor_stats.depth >= 30 and\n",
    "        somatic_stats.normal_stats.depth >= 30 and\n",
    "        somatic_stats.tumor_stats.alt_depth >= 5 and\n",
    "        (1 - somatic_stats.normal_stats.variant_allele_frequency) > 0.97\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "depth_to_filter_fn = {7: rizvi_filter, 0: no_filter, 30: filter_30}\n",
    "cohorts_with_bqsr = {}\n",
    "cohorts_without_bqsr = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overriding VCF dir (/home/tavi/ct/bladder/vcfs-indelrealigned-bqsr) to use without-BQSR VCFs: /home/tavi/ct/bladder/vcfs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overriding VCF dir (/home/tavi/ct/bladder/vcfs-indelrealigned-bqsr) to use without-BQSR VCFs: /home/tavi/ct/bladder/vcfs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overriding VCF dir (/home/tavi/ct/bladder/vcfs-indelrealigned-bqsr) to use without-BQSR VCFs: /home/tavi/ct/bladder/vcfs\n"
     ]
    }
   ],
   "source": [
    "for depth, filter_fn in depth_to_filter_fn.items():\n",
    "    cohorts_with_bqsr[filter_fn] = data.init_cohort(min_coverage_normal_depth=depth,\n",
    "                                                    min_coverage_tumor_depth=depth,\n",
    "                                                    print_provenance=False)\n",
    "    cohorts_without_bqsr[filter_fn] = data.init_cohort(\n",
    "        min_coverage_normal_depth=depth,\n",
    "        min_coverage_tumor_depth=depth,\n",
    "        without_bqsr=True, \n",
    "        cache_data_dir=\"/home/tavi/bladder-analyses/data/cache-without-bqsr\",\n",
    "        print_provenance=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from cohorts.model import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from utils.paper import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from numpy.random import seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "seed(1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "function_descriptions = {}\n",
    "function_descriptions[rizvi_filter] = \"Tumor/Normal Depth ≥ 7, Tumor VAF > 0.1, Normal VAF < 0.03\"\n",
    "function_descriptions[filter_30] = \"Tumor/Normal Depth ≥ 30, Tumor Alt Depth ≥ 5\"\n",
    "function_descriptions[no_filter] = \"No Depth/VAF Filters\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def description_formatter(func):\n",
    "    return function_descriptions[func]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_impact_mutations(cohort):\n",
    "    hugo_translation = pd.read_csv(path.join(data.REPO_DATA_DIR, 'hugo-ensembl-map.txt'), sep='\\t')[[\n",
    "            'Approved Symbol', 'Previous Symbols']].dropna()\n",
    "    hugo_translation_map = {}\n",
    "    for previous_symbols, symbol in zip(hugo_translation['Previous Symbols'], hugo_translation['Approved Symbol']):\n",
    "        previous_symbols = previous_symbols.split(',')\n",
    "        for old_symbol in previous_symbols:\n",
    "            hugo_translation_map[old_symbol] = symbol\n",
    "\n",
    "    impact_mutations = pd.read_csv(path.join(data.REPO_DATA_DIR, 'bladder-impact.txt'), sep='\\s+')\n",
    "    impact_mutations = impact_mutations[impact_mutations['Prot'].notnull()]\n",
    "    impact_mutations['effect_description'] = 'p.' + impact_mutations.Prot.str.upper()\n",
    "    impact_mutations.rename(columns={'Gene': 'gene_name', 'StudyID': 'patient_id'}, inplace=True)\n",
    "    impact_mutations['patient_id'] = impact_mutations['patient_id'].astype(str)\n",
    "    impact_mutations = impact_mutations[~impact_mutations['effect_description'].str.contains('FS')]\n",
    "    impact_mutations = impact_mutations[~impact_mutations['effect_description'].str.contains('DEL')]\n",
    "    impact_mutations = impact_mutations[~impact_mutations['effect_description'].str.contains('INS')]\n",
    "\n",
    "\n",
    "    gene_conversions = {\n",
    "        'MLL' : 'KMT2A',\n",
    "        'MLL2' : 'KMT2D',\n",
    "        'MLL3' : 'KMT2C',\n",
    "        'CDKN2Ap16INK4A' : 'CDKN2A',\n",
    "        'CDKN2Ap14ARF' : 'CDKN2A'\n",
    "    }\n",
    "\n",
    "    impact_mutations.gene_name = impact_mutations.gene_name.map(lambda g: gene_conversions.get(g, g))\n",
    "\n",
    "    impact_mutations.gene_name = impact_mutations.gene_name.map(lambda g: hugo_translation_map.get(g, g))\n",
    "\n",
    "    impact_mutations = impact_mutations[~impact_mutations.patient_id.isin(set(['4072', '7592']))]\n",
    "\n",
    "    PATIENTS_WITH_IMPACT = set(impact_mutations.patient_id).intersection(set(cohort.as_dataframe().patient_id))\n",
    "    return impact_mutations, PATIENTS_WITH_IMPACT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def calculate_precision(cohort, filter_fn=None):\n",
    "    impact_mutations, PATIENTS_WITH_IMPACT = get_impact_mutations(cohort)\n",
    "    variants = cohort.load_variants(filter_fn=filter_fn)\n",
    "    \n",
    "    from pyensembl import ensembl75\n",
    "    from varcode.effects import Substitution\n",
    "\n",
    "    def parse_impact_genes():\n",
    "        impact_df = pd.read_csv(path.join(data.REPO_DATA_DIR, 'impact_gene_list.csv'))\n",
    "        impact_ensembl_ids = {}\n",
    "\n",
    "        for gene_symbol in impact_df['Hugo_symbol']:\n",
    "            try:\n",
    "                gene_ids = ensembl75.gene_ids_of_gene_name(gene_symbol)\n",
    "                for id in gene_ids:\n",
    "                    impact_ensembl_ids[id] = gene_symbol\n",
    "            except:\n",
    "                continue\n",
    "        return impact_ensembl_ids\n",
    "\n",
    "    impact_ensembl_ids = parse_impact_genes()\n",
    "    def effect_impact_filter(filterable_effect):\n",
    "        if filter_fn is None:\n",
    "            return filterable_effect.effect.gene_id in impact_ensembl_ids\n",
    "        else:\n",
    "            return filter_fn(filterable_effect) and filterable_effect.effect.gene_id in impact_ensembl_ids\n",
    "    \n",
    "    effects = cohort.load_effects(only_nonsynonymous=True, filter_fn=effect_impact_filter)\n",
    "    from cohorts.variant_stats import variant_stats_from_variant\n",
    "    \n",
    "    def row_from_effect(effect):\n",
    "        row = {}\n",
    "        row['contig'] = effect.variant.contig\n",
    "        row['start'] = effect.variant.start\n",
    "        row['ref'] = effect.variant.ref\n",
    "        row['alt'] = effect.variant.alt\n",
    "        row['gene_id'] = effect.gene_id\n",
    "        row['gene_name'] = effect.gene_name\n",
    "        row['transcript_id'] = effect.transcript_id\n",
    "        row['transcript_name'] = effect.transcript_name\n",
    "        row['variant'] = str(effect.variant)\n",
    "        row['is_snv'] = effect.variant.is_snv\n",
    "        row['is_indel'] = effect.variant.is_indel\n",
    "        row['is_transversion'] = effect.variant.is_transversion\n",
    "        row['is_transition'] = effect.variant.is_transition\n",
    "        row['effect'] = str(effect)\n",
    "        row['effect_type'] = effect.__class__.__name__\n",
    "        row['effect_description'] = effect.short_description\n",
    "        row['nucleotide_change'] = effect.variant.ref + '>' + effect.variant.alt\n",
    "        return row\n",
    "\n",
    "    effects_sets = []\n",
    "    rows = []\n",
    "    for (sample, effect_collection) in effects.items():\n",
    "        sample_effects = effect_collection\n",
    "        for effect in sample_effects:\n",
    "            fv = FilterableVariant(effect.variant, variants[sample], cohort.patient_from_id(sample))\n",
    "            stats = variant_stats_from_variant(effect.variant, fv.variant_metadata)\n",
    "            row = row_from_effect(effect)\n",
    "            row['patient_id'] = sample\n",
    "            row['TumorVAF'] = stats.tumor_stats.variant_allele_frequency\n",
    "            row['TumorAltReads'] = stats.tumor_stats.alt_depth\n",
    "            row['TumorDepth'] = stats.tumor_stats.depth\n",
    "            row['NormalAltReads'] = stats.normal_stats.alt_depth\n",
    "            row['NormalDepth'] = stats.normal_stats.depth\n",
    "            rows.append(row)\n",
    "    effects_df = pd.DataFrame.from_records(rows)\n",
    "    effects_df = effects_df[effects_df.patient_id.isin(PATIENTS_WITH_IMPACT)]\n",
    "    \n",
    "    impact_mutations_all = impact_mutations.merge(\n",
    "                            effects_df, \n",
    "                            how='outer', \n",
    "                            on=['patient_id', 'gene_name', 'effect_description'])\n",
    "    # Prot = in IMPACT\n",
    "    validated_filter = (impact_mutations_all['Prot'].notnull())\n",
    "    # gene_id = in our filtered variants\n",
    "    found_filter = (impact_mutations_all['gene_id'].notnull())\n",
    "\n",
    "    impact_validated = impact_mutations_all[validated_filter]\n",
    "    impact_found = impact_mutations_all[found_filter]\n",
    "    impact_validated_found = impact_mutations_all[validated_filter & found_filter]\n",
    "    impact_validated_missed = impact_mutations_all[validated_filter & ~found_filter]\n",
    "    impact_wrong = impact_mutations_all[~validated_filter & found_filter]\n",
    "\n",
    "    validated_missense = len(impact_validated)\n",
    "    found_validated_missense = len(impact_validated_found)\n",
    "\n",
    "    recall = float(found_validated_missense)/validated_missense \n",
    "    precision = float(found_validated_missense)/len(impact_found) \n",
    "    \n",
    "    tp = len(impact_mutations_all[validated_filter & found_filter])\n",
    "    tn = len(impact_mutations_all[~validated_filter & ~found_filter])\n",
    "    # not in our set, but it is IMPACT\n",
    "    fn = len(impact_mutations_all[validated_filter & ~found_filter])\n",
    "    # in our set, but it is not IMPACT\n",
    "    fp = len(impact_mutations_all[~validated_filter & found_filter])\n",
    "    \n",
    "    assert precision == tp / float(tp + fp)\n",
    "    assert recall == tp / float(tp + fn)\n",
    "    \n",
    "    acc = (tp + tn) / float((fp + tp) + (fn + tn))\n",
    "\n",
    "    f1_score  = 2* (precision * recall )/ (precision + recall)\n",
    "\n",
    "    return (recall, precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join with ensembl_coverage: 25 to 25 rows\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney test: U=87.0, p-value=0.411702747006 (two-sided)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAGHCAYAAABMPA63AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ns48AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6eb9f73f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{with_bqsr_filter_30_auc:0.61, 95% CI (0.35, 0.85)}}}\n",
      "{{{with_bqsr_filter_30_mw:n=25, Mann-Whitney p=0.41}}}\n",
      "{{{with_bqsr_filter_30_recall:0.52}}}\n",
      "{{{with_bqsr_filter_30_precision:0.68}}}\n",
      "{{{with_bqsr_filter_30_auc_description:Tumor/Normal Depth ≥ 30, Tumor Alt Depth ≥ 5}}}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join with ensembl_coverage: 25 to 25 rows\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney test: U=94.0, p-value=0.223528962588 (two-sided)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAGHCAYAAABMPA63AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6eac972910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{with_bqsr_rizvi_filter_auc:0.65, 95% CI (0.43, 0.85)}}}\n",
      "{{{with_bqsr_rizvi_filter_mw:n=25, Mann-Whitney p=0.22}}}\n",
      "{{{with_bqsr_rizvi_filter_recall:0.49}}}\n",
      "{{{with_bqsr_rizvi_filter_precision:0.72}}}\n",
      "{{{with_bqsr_rizvi_filter_auc_description:Tumor/Normal Depth ≥ 7, Tumor VAF > 0.1, Normal VAF < 0.03}}}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join with ensembl_coverage: 25 to 25 rows\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney test: U=91.0, p-value=0.294842421401 (two-sided)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAGHCAYAAABS9T4EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nICIiKAxERASFgYiIAP8FBU7HvYLUVAkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6eb880f990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{with_bqsr_no_filter_auc:0.63, 95% CI (0.36, 0.86)}}}\n",
      "{{{with_bqsr_no_filter_mw:n=25, Mann-Whitney p=0.29}}}\n",
      "{{{with_bqsr_no_filter_recall:0.63}}}\n",
      "{{{with_bqsr_no_filter_precision:0.68}}}\n",
      "{{{with_bqsr_no_filter_auc_description:No Depth/VAF Filters}}}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join with ensembl_coverage: 25 to 25 rows\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney test: U=91.0, p-value=0.294935298757 (two-sided)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAGHCAYAAABMPA63AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIcifgLkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6eba525ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{without_bqsr_filter_30_auc:0.63, 95% CI (0.38, 0.86)}}}\n",
      "{{{without_bqsr_filter_30_mw:n=25, Mann-Whitney p=0.29}}}\n",
      "{{{without_bqsr_filter_30_recall:0.52}}}\n",
      "{{{without_bqsr_filter_30_precision:0.68}}}\n",
      "{{{without_bqsr_filter_30_auc_description:Tumor/Normal Depth ≥ 30, Tumor Alt Depth ≥ 5}}}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join with ensembl_coverage: 25 to 25 rows\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney test: U=104.0, p-value=0.0745315710546 (two-sided)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAGHCAYAAABMPA63AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6eadce5ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{without_bqsr_rizvi_filter_auc:0.72, 95% CI (0.51, 0.90)}}}\n",
      "{{{without_bqsr_rizvi_filter_mw:n=25, Mann-Whitney p=0.075}}}\n",
      "{{{without_bqsr_rizvi_filter_recall:0.5}}}\n",
      "{{{without_bqsr_rizvi_filter_precision:0.63}}}\n",
      "{{{without_bqsr_rizvi_filter_auc_description:Tumor/Normal Depth ≥ 7, Tumor VAF > 0.1, Normal VAF < 0.03}}}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join with ensembl_coverage: 25 to 25 rows\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney test: U=103.0, p-value=0.0842175169115 (two-sided)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAGHCAYAAABS9T4EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzQ9VZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6eba5cead0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{without_bqsr_no_filter_auc:0.72, 95% CI (0.48, 0.90)}}}\n",
      "{{{without_bqsr_no_filter_mw:n=25, Mann-Whitney p=0.084}}}\n",
      "{{{without_bqsr_no_filter_recall:0.65}}}\n",
      "{{{without_bqsr_no_filter_precision:0.53}}}\n",
      "{{{without_bqsr_no_filter_auc_description:No Depth/VAF Filters}}}\n"
     ]
    }
   ],
   "source": [
    "for cohorts in [cohorts_with_bqsr, cohorts_without_bqsr]:\n",
    "    cohort_name = \"with_bqsr\" if cohorts == cohorts_with_bqsr else \"without_bqsr\"\n",
    "    for filter_fn, cohort in cohorts.items():\n",
    "        series = cohort_bootstrap_auc(cohort, on=missense_snv_count, filter_fn=filter_fn)\n",
    "        benefit_results = cohort.plot_benefit(on=missense_snv_count, filter_fn=filter_fn)\n",
    "        plt.show()\n",
    "        recall, precision = calculate_precision(cohort, filter_fn=filter_fn)\n",
    "        hyper_label_printer(bootstrap_mean_formatter, label=(\"%s_%s_auc\" % (cohort_name, filter_fn.__name__)),\n",
    "            series=series)\n",
    "        hyper_label_printer(mann_whitney_formatter, label=(\"%s_%s_mw\" % (cohort_name, filter_fn.__name__)),\n",
    "            results=benefit_results)\n",
    "        hyper_label_printer(float_str, label=(\"%s_%s_recall\" % (cohort_name, filter_fn.__name__)),\n",
    "            f=recall)\n",
    "        hyper_label_printer(float_str, label=(\"%s_%s_precision\" % (cohort_name, filter_fn.__name__)),\n",
    "            f=precision)\n",
    "        hyper_label_printer(description_formatter, label=(\"%s_%s_auc_description\" % (cohort_name, filter_fn.__name__)),\n",
    "            func=filter_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
